基于序列信息和机器学习的蛋白质功能预测研究进展

doi:10.16178/j.issn.0528-9017.20250739

摘要/Abstract

摘要：

随着计算能力的增加和生物数据的快速扩展，蛋白质测序技术得到快速进步，数据库中的蛋白质数量急剧增加。蛋白质数据复杂多样，使得蛋白质功能预测成为一个极具挑战性的问题，得到了广泛关注。同时，随着人工智能的迅速发展，机器学习等方法逐渐被运用到蛋白质功能预测中。近年来，国内外科研人员在蛋白质功能预测领域不断探索，取得了丰富的研究成果。本文对基于生物信息学的蛋白质序列信息的功能预测方法进行归纳，并进一步分析和总结了这些方法的具体算法以及最新研究进展，最后对蛋白质功能预测存在的问题进行讨论，并对该领域未来的研究方向进行了展望。

关键词: 生物信息学, 机器学习, 蛋白质, 功能预测

Abstract:

With the rapid advancement of computational power and the expansion of biological data，protein sequencing technology has seen significant progress，leading to a sharp increase in the number of proteins within databases. Due to the complexity and diversity of protein data，functional prediction has become a highly challenging issue and has garnered widespread attention. Concurrently，with the rapid development of artificial intelligence，methods such as machine learning have been progressively applied to protein function prediction. In recent years，researchers domestically and internationally have been continuously exploring this field，achieving substantial research outcomes. This paper summarized the functional prediction methods of protein sequence information based on bioinformatics. Furthermore，we analyzed and summarized the specific algorithms and recent advances within these approaches. Finally，we discussed existing challenges in protein function prediction and provided an outlook on future research directions in this domain.

Key words: bioinformatics, machine learning, protein, function prediction

中图分类号:

吴健雄, 鲍玉峰. 基于序列信息和机器学习的蛋白质功能预测研究进展[J]. 浙江农业科学, 2026, 67(2): 552-556.

WU Jianxiong, BAO Yufeng. Study progress of protein function prediction based on sequence information and machine learning[J]. Journal of Zhejiang Agricultural Sciences, 2026, 67(2): 552-556.

图/表 4

参考文献 23

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发表年份	方法	模型评估标准
发表年份	方法	BP	CC	MF
2020	DeepGOPlus	F_max=0.390	F_max=0.614	F_max=0.557
2021	NCL+mask BLAST	F₁=0.378	F₁=0.475	F₁=0.496
2022	Wei2GO	F_max=0.520	F_max=0.580	F_max=0.390

发表年份	方法	模型评估标准
发表年份	方法	BP	CC	MF
2020	DeepGOPlus	F_max=0.390	F_max=0.614	F_max=0.557
2021	NCL+mask BLAST	F₁=0.378	F₁=0.475	F₁=0.496
2022	Wei2GO	F_max=0.520	F_max=0.580	F_max=0.390

发表年份	方法	模型评估标准
发表年份	方法	BP	CC	MF
2018	GOLabeler	F_max=0.372 AUPR=0.236	F_max=0.586 AUPR=0.697	F_max=0.691 AUPR=0.549
2020	DeepAdd	F_max=0.345 AUC=0.896	F_max=0.547 AUC=0.958	F_max=0.516 AUC=0.912
2020	FFPred-GAN	F_max=0.567	F_max=0.755	F_max=0.750
2021	Global-ProtEnc	F_max=0.523	F_max=0.636	F_max=0.515
2022	FUTUSA	F₁=0.532	—	—
2022	GAT-GO	F_max=0.501 AUPR=0.381	F_max=0.542 AUPR=0.479	F_max=0.637 AUPR=0.660
2022	SPROF-GO	F_max=0.335 AUPR=0.247	F_max=0.725 AUPR=0.765	F_max=0.647 AUPR=0.622
2023	ProteInfer	F_max=0.647 AUPR=0.622	F_max=0.335 AUPR=0.247	F_max=0.647 AUPR=0.622
2023	PFP-SCGCN	F_max=0.706	F_max=0.796	F_max=0.787
2024	AnnoPRO	F_max=0.609 AUPR=0.574	F_max=0.746 AUPR=0.749	F_max=0.763 AUPR=0.755
2024	TransFew	F_max=0.449 AUPR=0.244	F_max=0.726 AUPR=0.455	F_max=0.666 AUPR=0.363
2024	PU-GO	F_max=0.556	F_max=0.734	F_max=0.569
2024	GORetriever	F_max=0.545	F_max=0.653	F_max=0.659

发表年份	方法	模型评估标准
发表年份	方法	BP	CC	MF
2018	GOLabeler	F_max=0.372 AUPR=0.236	F_max=0.586 AUPR=0.697	F_max=0.691 AUPR=0.549
2020	DeepAdd	F_max=0.345 AUC=0.896	F_max=0.547 AUC=0.958	F_max=0.516 AUC=0.912
2020	FFPred-GAN	F_max=0.567	F_max=0.755	F_max=0.750
2021	Global-ProtEnc	F_max=0.523	F_max=0.636	F_max=0.515
2022	FUTUSA	F₁=0.532	—	—
2022	GAT-GO	F_max=0.501 AUPR=0.381	F_max=0.542 AUPR=0.479	F_max=0.637 AUPR=0.660
2022	SPROF-GO	F_max=0.335 AUPR=0.247	F_max=0.725 AUPR=0.765	F_max=0.647 AUPR=0.622
2023	ProteInfer	F_max=0.647 AUPR=0.622	F_max=0.335 AUPR=0.247	F_max=0.647 AUPR=0.622
2023	PFP-SCGCN	F_max=0.706	F_max=0.796	F_max=0.787
2024	AnnoPRO	F_max=0.609 AUPR=0.574	F_max=0.746 AUPR=0.749	F_max=0.763 AUPR=0.755
2024	TransFew	F_max=0.449 AUPR=0.244	F_max=0.726 AUPR=0.455	F_max=0.666 AUPR=0.363
2024	PU-GO	F_max=0.556	F_max=0.734	F_max=0.569
2024	GORetriever	F_max=0.545	F_max=0.653	F_max=0.659